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:: Course SummaryThis hands-on workshop offers an introduction to the fundamental principles and concepts in statistics.
The first part covers classical and more recent exploratory data analysis (EDA) techniques to describe data with numerical and graphical tools. The various uses of these methods like outlier detection is presented.
The second part addresses, with the help of real-life examples, the principles underlying statistical testing and decision-making in the presence of uncertainty. It covers risks involved (alpha and beta), p-values and statistical significance. The use and interpretation of confidence intervals is also discussed.
This course can serve as an introductory class or a refresher and provides a solid basis for all other courses. :: Learning ObjectivesUpon completion of this course, participants will be able to:
Understand the difference between descriptive and inferential statistics
Appreciate the value of exploratory methods in preliminary data analysis
Explore, characterize and identify problems and trends in data using graphical tools
Use descriptive statistics to summarize data
Master the concepts of hypothesis testing, confidence intervals, risk and power
Perform the appropriate statistical test based on the study objective
Analyze data more quickly and more accurately
Interpret results reliably and confidently:: Target AudienceThis applied training session in statistics is aimed at all who collect data and who must make decisions based on that data.:: PrerequisiteThis course introduces the important ideas in statistics and data analysis. It assumes that participants either have no previous knowledge of statistics or that they have not used statistics for a long time.
Register for Fundamentals Tools in Statistics as well as Introduction to the Design of Experiments
through the bundle The Fundamentals of Design of Experiments (DOE) and save on the training fees.:: Notes and Other InformationSome association members are entitled to discounted registration fees. During the online registration process, do not forget to mention the association name, your membership number and the registration fees will be adjusted if you are entitled to a discount. | | |
:: Topics Covered
- Introduction to Statistics
- Descriptive Data Analysis
- Why Do we Need Statistics? Overview of Descriptive or Exploratory Data Analysis
- Type and Role of Variables in Studies and Experiments
- Visualizing and Summarizing Data: The Concept of a Distribution
- Characterizing Distributions with Numerical and Graphical Tools: mean, median, standard deviation, standard error, histogram, Box-plot, etc.
- Exploring the Relationship between Two variables: Scatter Plots, Correlation Coefficients, Frequency Tables
- Statistical Inference or Statistical Testing
- Overview: What is Statistical Inference?
- Statistical Inference with Hypothesis Testing: null and alternative hypotheses, one-tailed vs. two-tailed tests, test statistics, p-value, statistical significance, decision rules
- The Concept of Risk and Power: risks involved, type I and II errors, confidence level and power of test
- Statistical Inference with Confidence Intervals: how it works, when to use it
- Equivalence of the Hypothesis Testing and the Confidence Interval Approaches
- Statistical Inference for a Single Sample or Group: Hypothesis Testing vs. Confidence Interval Approach
- Summary
:: Course ContentThis one-day training course reviews the most important basic concepts in statistics for R&D. The course begins with an overview of the role of statistics in R&D. The types and roles of variables are discussed along with tools for characterizing and summarizing variables and for exploring the relationship between them.
Among the graphical tools presented for visualizing data are: the histogram, the box-plot and the scatter plot.
The course then turns to inferential statistics and describes the purpose, elements and scope of statistical tests: definition of a statistical test, what a p-value is, the risks associated with statistical tests and how these risks can be minimized.
Finally, we discuss confidence intervals and what is meant by "confidence". We cover what confidence intervals are, how to interpret them and the equivalence between confidence intervals and hypothesis testing.
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